Stock market prediction is one of the areas that had been very interesting for investors, economists and managers. For this purpose, classical and modern methods such as AR and ARIMA models, Neural Networks, GA, Fuzzy Logic, etc, have been proposed but among them NNs play an essential role. In this paper, the ability of three different neural networks, namely MLP, RBF and GRNN, are compared for stock market prediction. Unknown parameters of each network are optimized for minimum error by GA in training phase. Then trained networks are used for prediction of two and three monthly returns. In addition, for the first time in the literatures, the optimum order for each model, i.e. the number of input variables for each NN model is determined using trial and error.

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